2026-07-24

Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning

第一作者: Nicolas Kosanovic · 方向: 机器人操作 · 来源: cs.RO

07/23 01:35

小型人形机器人远程操作虚拟现实强化学习控制栈隐藏英文摘要

Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lower-body balance and locomotion control. As a result, a single remote operator can see, manipulate, and navigate about a real, distant physical environment. This powerful control stack is often relegated to expensive full-sized robots, many of which are inaccessible to the research community. Miniature humanoids are more prevalent, but employ less biomimicry in their design (e.g. fewer sensors, Degrees of Freedom, etc) and lack similar developments. This paper describes a compliant full-body telepresence control stack developed from the ground up for miniature humanoids. Framework experimentation on ROBOTIS OP3...

论文介绍 小型人形机器人缺乏类似全尺寸机器人的远程操作控制。本文从头开发兼容全身远程操作控制栈，结合虚拟现实用于上体远程操作和强化学习用于下体平衡控制，实现单操作员导航和操作。该框架促进了机器人研究和教育中的可访问性。

Distributed Acoustic Localization Array Deployed Using a Soft Everting Vine Robot

第一作者: Sebastian Lorca Godoy · 方向: 具身智能 · 来源: cs.RO

07/23 01:26

软机器人声学定位分布式感知灾难救援麦克风阵列隐藏英文摘要

Soft robot exteroception is increasingly being explored for a variety of field applications. In this work, we present a sound-based system for localizing disaster victims in confined and unstructured environments, based on a distributed acoustic sensing architecture embedded along the body of a soft everting vine robot. We propose a dynamic Steered Response Power with Phase Transform framework that supports both far-field direction-of-arrival estimation and near-field three-dimensional source localization as the robot approaches the sound source. To better understand the design and control space related to localizing sound using a soft, shape-morphing robot body, we conduct experiments measuring the accuracy of these methods for a five-microphone array attached to the robot body using three placements relative to the outer membrane of the robot (inside the pressurized body, inside the...

论文介绍 在受限和非结构化环境中，基于声音定位灾难受害者至关重要。本文提出基于软藤蔓机器人的分布式声学传感系统，嵌入麦克风阵列并使用动态SRP-PHAT框架实现方向估计和三维定位。该系统适用于灾难救援等应用场景。

Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats

第一作者: Alejandro Gonzalez-Garcia · 方向: 具身智能 · 来源: cs.RO

07/23 00:37

自重构机器人分布式规划模型预测控制控制屏障函数多代理系统隐藏英文摘要

Aquatic self-reconfigurable robots must assemble into desired shapes while ensuring safe interactions among multiple agents. This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs) for multi-agent shape formation and reconfiguration. Given a desired shape and target assignment, a distributed MPC scheme, solved via the Alternating Direction Method of Multipliers (ADMM), computes coordinated trajectories through local optimization and information exchange. To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent collision avoidance. The proposed approach leverages the predictive capabilities of MPC to mitigate local minima, while CBFs provide formal safety guarantees despite the nonconvexity of the underlying optimization problem. Simulation results with up to 25 agents...

论文介绍 水生自重构机器人的形状形成和重配置需确保多代理安全交互。本文提出混合框架，结合分布式模型预测控制和控制屏障函数，通过ADMM求解协调轨迹并实时避免碰撞。该方法提供形式化安全保证，适用于复杂环境中的机器人编队。

Robots Acquire Manipulation Skills in Seconds from a Single Human Video

第一作者: Guangyan Chen · 方向: 机器人操作 · 来源: cs.RO

07/22 19:20

机器人技能获取单次学习人机视频HOST框架预测级联隐藏英文摘要

The ability to acquire skills rapidly and effortlessly while retaining those already mastered is essential for robots. However, current methods still rely on a cumbersome training-time loop that is costly and slow, while eroding skills already mastered. In this paper, we introduce HOST (Human-to-robot One-Shot Skill AcquisiTion), a framework that enables a robot to acquire skills in seconds from a single human video while retaining previously mastered skills. HOST resolves skill acquisition through a cascade of self-grounded prediction. It first estimates the robot's progress within the demonstrated task, then translates the upcoming progression into the robot's own future observations, and finally derives actions from these predicted observations. This cascade is trained on targets coupled to the video demonstration, obtained by mapping the robot trajectory and the video demonstration...

论文介绍 研究机器人如何快速获取操作技能并保留已有技能。提出HOST框架，从单次人类演示视频中在秒级内学习技能，通过自接地预测级联估计任务进度、转换机器人未来观察并派生动作，旨在提高技能学习效率和保持已有技能。

LENS: LLM-guided Environment Simplification for Planning and Control in Clutter

第一作者: Aileen Liao · 方向: 机器人操作 · 来源: cs.RO

07/22 08:05

LLM引导环境简化杂乱场景规划与控制场景抽象LENS框架隐藏英文摘要

Despite recent advances in general-purpose robotic manipulation, real-world multi-object clutter remains challenging to handle for today's prevalent approaches. The problem scales in complexity due to more objects and collisions, more unpredictable contact physics, distractors, and task ambiguity. Bridging this gap to real-world deployment requires effective scene abstractions; yet today, producing such abstractions requires extensive task-specific manual engineering, which does not scale. These abstractions are costly to generate and difficult to adjust or fine-tune. We instead propose a plug-and-play fix to automatically generate scene-specific, task-specific, adaptively updating abstractions on top of existing planning and control stacks. LLM-guided Environment Simplification (LENS) produces a de-cluttered abstracted scene representation by merging (e.g., stacked objects) or pruning...

论文介绍 针对杂乱环境中的机器人规划和控制挑战，现有方法依赖手动场景抽象，成本高且不灵活。提出LENS方法，利用大语言模型自动生成场景特定的抽象表示，通过合并或修剪物体简化场景，作为即插即用模块提升规划栈的适应性和部署效率。

HypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems

第一作者: Nicolò Botteghi · 方向: 策略学习 · 来源: cs.LG

07/22 07:37

超网络集成强化学习参数化动力系统鲁棒控制HypEMBER框架隐藏英文摘要

In this work we investigate reinforcement learning (RL) as a framework for the robust control of parametrized dynamical systems in presence of measurements and model uncertainties. High-dimensional state spaces, expensive numerical solvers, the partial knowledge of the governing equations, and the dependence on physical parameters that may be uncertain or difficult to estimate accurately, make the use of standard RL approaches computationally unfeasible. Indeed, lack of robustness and poor generalization across parameter variations are further amplified in presence of noisy or incomplete measurements, ultimately hampering control performance. To address these challenges, we introduce HypEMBER, a novel RL framework based on the combination of hypernetworks and ensemble learning. In the proposed approach, both the policy and value functions are represented through hypernetworks that...

论文介绍 探索强化学习在参数化动力系统鲁棒控制中的应用，针对高维状态、模型不确定性和测量噪声问题。引入HypEMBER框架，结合超网络和集成学习表示策略和价值函数，旨在提升控制策略的鲁棒性和跨参数变化的泛化能力。

ModPack: An Extensible Teleoperation Interface for Bimanual Mobile Manipulation

第一作者: Joshua Citron · 方向: 机器人操作 · 来源: cs.RO

07/22 02:02

模块化遥操作系统双臂移动操作可扩展接口ModPack系统数据收集隐藏英文摘要

Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability. We present ModPack, a modular and extensible teleoperation system designed to support diverse robot embodiments and task requirements within a unified framework. At the core of ModPack is a self-contained wearable "backpack" that integrates onboard computation, power, communication, and data storage. Built on top of this shared interface, the system supports plug-and-play capability modules including joint-level teleoperation with haptic feedback, mobile manipulation, and active perception. Experiments across two distinct robot platforms and real-world mobile manipulation tasks demonstrate that ModPack provides a flexible and reusable framework for data collection and policy learning. To support future research, we open-source the complete hardware...

论文介绍 现有遥操作系统往往针对特定硬件和任务，限制可扩展性。提出ModPack系统，一个模块化、可扩展的遥操作框架，核心是可穿戴背包集成计算和通信，支持插件式功能模块如关节遥操作和移动操作，适用于多样机器人平台的数据收集和策略学习。

Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids

第一作者: Roger Sala Sisó · 方向: VLA 通用模型 · 来源: cs.RO

数据高效后训练经验驱动学习视觉-语言-动作模型人形机器人零售应用隐藏英文摘要

Abstract:Closing the gap between benchmark performance and reliable real-world operation remains a central challenge for Vision-Language-Action (VLA) humanoid robots, which must handle execution errors, distribution shifts, and environmental variability. This paper presents DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level approach evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid robot and the GR00T N1.6 foundation model. DEED comprises three key components: (1) a data-efficient post-training pipeline with control-frequency alignment, data curation, task-relevant visual highlighting, and reduced VLA dependence; (2) a real-world study of experience-driven refinement, adapted from RECAP via a text-based advantage prefix and a vision-language value function; and (3) a latent-space analysis tool for studying in- and...

论文介绍 针对视觉-语言-动作人形机器人在真实世界中的性能差距，提出DEED框架，用于超市补货任务。包含数据高效后训练流程、经验驱动细化机制和潜在空间分析工具，旨在减少数据需求并提高机器人在零售环境中的部署可靠性。

Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments

第一作者: Md Ridwan Hossain Talukder · 方向: 具身智能 · 来源: cs.RO

礼貌预期规划多机器人任务规划共享环境成本最小化模型基规划器隐藏英文摘要

Abstract:We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence. Standard task planners, lacking foresight of future tasks and inconsiderate of others' constraints, solve each task in isolation, leaving terminal states that increase future cost for all, side effects that compound over lengthy task sequences. To reduce cost over the sequence, a robot must anticipate how its actions now may impact performance on future tasks for all robots sharing the environment. Therefore, we present courteous anticipatory planning, wherein a model-based planner proposes candidate plans and selects the one that jointly minimizes immediate cost and aggregated expected future cost across all robots, estimated via independent per-robot learned estimators. This factored formulation avoids combinatorial joint rollouts...

论文介绍 考虑多机器人共享持久环境中的长期任务规划，标准规划器忽略未来任务导致成本累积。提出礼貌预期规划方法，通过模型基规划器选择最小化即时和未来总成本的计划，使用独立学习估计器避免组合爆炸，以降低序列任务总成本。

SeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments

第一作者: Yang Xu · 方向: 机器人操作 · 来源: cs.RO

语言引导抓取复杂场景多机器人形态种子点预测数据高效框架隐藏英文摘要

Abstract:Practical robotic grasping in complex scenes requires both 3D spatial reasoning and alignment with task-specific requirements. Vision-language models (VLMs) offer a natural way to specify these requirements using language, but existing approaches either use a VLM to predict the grasp directly with limited spatial awareness, or train the VLM together with the grasping model, which requires significantly more data and compute. These limitations impede performance and have prevented scaling to multiple embodiments in complex scenes. We address this by proposing SeededGrasp, a novel data-efficient framework that enables a VLM to predict a seed point to be used as conditioning for a subsequent lightweight grasp-generation model. Our architecture decouples high-level semantic reasoning from low-level geometric execution, enabling multi-embodiment support while bypassing the need for...

论文介绍 复杂场景中的机器人抓取需结合3D空间推理和任务对齐。提出SeededGrasp框架，利用视觉-语言模型预测种子点作为条件，输入轻量抓取生成模型，解耦语义推理和几何执行，支持多种机器人形态并提高数据效率和场景适应性。

ReferTrack: Referring Then Tracking for Embodied Visual Tracking

第一作者: Hanjing Ye · 方向: VLA 通用模型 · 来源: cs.RO

指代跟踪具身视觉跟踪自然语言描述滑动窗口队列ReferTrack模型隐藏英文摘要

Abstract:Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning often operates in abstract spatial latents that are difficult to supervise and weakly aligned with explicit image-space detections. To address this, we introduce ReferTrack, a referring-then-tracking paradigm that grounds EVT using a single forward-facing camera. Our model first selects the target from an indexed set of bounding boxes, then decodes tracking waypoints conditioned on this image-grounded decision. To preserve target motion cues over time, ReferTrack maintains a sliding-window queue of previously selected bounding boxes, injecting their geometric features into the visual...

论文介绍 具身视觉跟踪中，现有视觉-语言-动作策略的目标识别与轨迹规划常存在对齐问题。提出ReferTrack范式，先从边界框中指代目标再跟踪，通过滑动窗口队列保持运动线索，使用单前向摄像头实现基于语言描述的跟踪，提高监督对齐性。

Unified Prediction and Planning via Conflict-Aware Disjoint Parameter Training

第一作者: Taewon Seo · 方向: 导航与运动 · 来源: cs.RO

运动预测运动规划参数冲突模型合并社会机器人导航隐藏英文摘要

Abstract:Accurate motion prediction of surrounding agents and safe motion planning are two closely coupled key tasks for social robot navigation in crowded environments. Deploying these systems on resource-constrained edge devices necessitates compact, unified models that can perform both tasks simultaneously. However, within these compact shared encoders, recent unified models often overlook severe representational conflicts that arise from the distinct objectives of predicting neighbor behaviors versus ego-centric safety planning. To address this issue, we first identify the Skill Conflict$\unicode{x2014}$a phenomenon where overlapping parameter assignments cause distinct tasks to compete for the same weights, preventing the model from fully specializing in individual skills. To resolve this, we propose a novel model-merging-based framework, Disjoint Parameter Training (DPT). DPT...

论文介绍 该研究关注在资源受限设备上统一执行运动预测与规划的紧凑模型所面临的「技能冲突」问题，即共享编码器中的参数竞争阻碍了模型对特定任务的深度学习。为此，论文提出了一种新颖的「不相交参数训练」框架，旨在通过模型合并技术解决此问题，以实现更高效、统一的机器人导航系统。

Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction

第一作者: Seonsoo Kim · 方向: 策略学习 · 来源: cs.RO

扩散模型序列预测可修订去噪机器人规划长程任务隐藏英文摘要

Abstract:We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons. Existing diffusion-based sequence predictors typically perform a single monotonic denoising process. In contrast, Diffusion ReRoll selectively re-noises regions that have become locally stable while the remaining regions continue denoising, so the re-noised regions can be refined again using context from the rest of the horizon. This structured re-noising enables iterative cross-horizon revision, allowing earlier and later segments to revise one another, while maintaining local consistency. We evaluate Diffusion ReRoll against full-sequence diffusion and causal denoising based on Diffusion Forcing across long-horizon planning, policy learning, and unified video-action modeling. On OGBench PointMaze and AntMaze, Diffusion ReRoll achieves...

论文介绍 现有基于扩散的机器人序列预测器通常执行单向、不可修订的去噪过程，限制了跨时间范围的修正能力。本文提出「Diffusion ReRoll」框架，其核心是允许在去噪过程中对已局部稳定的区域进行选择性重新加噪，从而利用未来上下文进行迭代修订，最终在长程规划、策略学习等任务中展现出优势。

EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness

第一作者: Jialu Zhang · 方向: 导航与运动 · 来源: cs.RO

跨具身导航具身感知模仿学习视觉导航预训练隐藏英文摘要

Abstract:Cross-embodiment navigation is a key challenge in embodied intelligence. Due to differences in embodiment, the same visual observation may imply different actions for different agents, making prediction ambiguous when relying solely on vision. Existing studies mainly rely on reinforcement learning, which requires large-scale interaction and careful reward design, making it difficult to support scalable pretraining and real-world adaptation. In contrast, imitation-learning-based approaches remain limited. To address these challenges, we propose an imitation-learning-based embodiment-aware navigation framework with a modular multi-stage design. In pretraining, we construct a cross-embodiment navigation dataset from Internet videos and introduce embodiment geometry as conditional tokens to reduce action ambiguity under the same observation. In fine-tuning, we design a multimodal...

论文介绍 跨具身导航面临因机器人形态差异导致的动作歧义挑战。现有强化学习方法难以扩展，而基于模仿学习的研究尚不充分。本文提出一个基于模仿学习的具身感知导航框架，通过模块化设计，在预训练阶段引入「具身几何」条件令牌来减少动作歧义，并在微调阶段整合多模态信息，旨在提升不同形态机器人的导航能力。

SOPD-SocialNav: Selective On-Policy Distillation for Vision-Language Social Navigation

第一作者: Xinyu Zhang · 方向: 导航与运动 · 来源: cs.RO

视觉语言模型知识蒸馏社会导航机器人导航模型压缩隐藏英文摘要

Abstract:Vision-language models have shown strong potential for social robot navigation by leveraging rich semantic understanding of complex environments and human behaviors. However, large scale VLMs are difficult to deploy on resource-constrained robotic platforms, while lightweight VLMs often lack sufficient social reasoning capability. To address this problem, we propose SOPD-SocialNav, a selective on-policy distillation (SOPD) method that transfers social navigation knowledge from a large teacher VLM to a lightweight student VLM. SOPD introduces an entropy-based token selection mechanism that uses teacher uncertainty to identify socially informative decision tokens, while suppressing gradients from low-entropy tokens corresponding to trivial navigation states. A temperature-controlled Jensen-Shannon divergence objective is then used to align the student and teacher distributions...

论文介绍 大规模视觉语言模型虽具备强大的社会推理能力，但难以部署在机器人平台上；轻量级模型则能力不足。为解决此矛盾，论文提出「选择性在策略蒸馏」方法，从大型教师模型向小型学生模型迁移社会导航知识。该方法利用基于熵的token选择机制，专注于提取关键决策信息，并通过温度控制的散度目标对齐分布。